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This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical...
The forecasting demand is the basis of strategic planning for production, sales and finances of any company. They are used for planning and control of production for planning productive system (long term) and the using (short term) of this system. With the increasing of the competition in the automobile market, there are, consequently, the increasing of concerning about establishing a balance between...
Artificial Neural Networks (ANN) have been widely used in time series forecasting problem. However, a more promising approach is the combination of ANN with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc, where these evolutionary algorithms have the objective of train and adjust all parameter of the ANN. In the evolutionary process is necessary define a fitness...
Artificial neural networks (ANN) have been paramount for modeling and forecasting time series phenomena. In this way it has been usual to suppose that each ANN model generates a white noise as prediction error. However, mostly because of disturbances not captured by each model, it is yet possible that such supposition is violated. On the other hand, to adopt a single ANN model may lead to statistical...
In this article a number of neural networks based on self organizing maps, that can be successfully used for dynamic object identification, is described. The structure and algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results is given.
Researchers have been challenged to combine time series forecasting models, with the intention of enhancing forecast accuracy and efficiency. In this way, to weight models accuracy, efficiency, and mutual dependency becomes paramount. A promising way to address this issue is via copulas. Copulas are joint probability distribution functions aimed to envelop both the marginal distribution as well as...
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